Date	Topics	Readings
Aug 26: Lecture 1	Overview of Machine Learning, Model selection, Over-fitting. Review of Probability Theory. Assignment 1: Solve the following exercises from the textbook: 1.3, 1.5, 1.6, 1.11, 1.13, 1.21. For Exercise 1.6, you can assume discrete random variables.	Slides are posted on Blackboard Chapter 1: Introduction and Sections 1.2 (up to 1.2.5) Learn more about the No Hands Across America project
Sept 2: Lecture 2	Quiz 1: probability Classification and Regression examples, Bayesian probabilities
Sept 9: Lecture 3	ML estimation, MAP, Curse of dimensionality, Decision theory. Solutions of quiz 1. Assignment 1 due Assignment 2 out	Sections 1.1, 1.3, 1.4, 1.5
Sept 16: Lecture 4	Linear models for classification, discriminant functions, Fisher's linear discriminant; Perceptron; Solutions of Assignment 1. Quiz 2	Section 4.1
Sept 23: Lecture 5	Probabilistic generative models; Probabilistic discriminative models; Solutions of quiz 2. Assignment 2 due	Sections 4.2, 4.3
Sept 30: Lecture 6	Logistic Regression (multiclass case); Backpropagation Quiz 3 Assignment 3 out	Backpropagation: Sections 5.1, 5.2, 5.3
Oct 7: Lecture 7	Project: Milestone 1 due (postponed to Oct 14) Principal Component Analysis; Kernel Methods
Oct 14: Lecture 8	Assignment 3 due Support Vector Machines	Handout
Oct 21: Lecture 9	Quiz 4 Assignment 4 out Support Vector Machines (part 2) One-class SVMs	Practice exercises for the midterm exam
Oct 28: Lecture 10	Midterm
Nov 4: Lecture 11	Quiz 5 Deep Learning: Convolutional Neural Networks CNNs and NLP
Nov 11: Lecture 12	Project: Milestone 2 due Assignment 4 due Clustering Kernel K-means
Nov 18: Lecture 13	Quiz 6
Nov 25:	Thanksgiving Recess - No Class!
Dec 2: Lecture 14	TBD
Dec 9:	Project: Milestone 3 due